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Engineered mRNAs With Stable Structures Minimize Double-stranded RNA Formation and Increase Protein Expression.

Qianshan Qin1, Huayuan Yan2, Weixiang Gao2

  • 1State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Suzhou Abogen Biosciences Co., Ltd., Suzhou, Jiangsu 215123, China.

Journal of Molecular Biology
|October 20, 2024
PubMed
Summary

Synthetic messenger RNA (mRNA) therapeutics face challenges from double-stranded RNA (dsRNA) byproducts. This study identifies sequence features that predict dsRNA formation, enabling optimized mRNA design for improved efficacy and reduced immune response.

Keywords:
RNA immunogenicityRNA purificationdouble-stranded RNAin vitro transcriptionmessenger RNA

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Area of Science:

  • Molecular Biology
  • Biotechnology
  • Vaccine Development

Background:

  • Synthetic messenger RNA (mRNA) therapeutics show promise for infectious and oncological vaccines.
  • Double-stranded RNA (dsRNA) byproducts during in vitro transcription (IVT) reduce mRNA efficacy and trigger immune responses.
  • Current dsRNA removal methods are inefficient and labor-intensive.

Purpose of the Study:

  • To identify sequence characteristics influencing dsRNA byproduct formation during mRNA IVT.
  • To develop a predictive model for dsRNA byproduct formation.
  • To guide mRNA sequence optimization for enhanced therapeutic efficacy.

Main Methods:

  • Analysis of mRNA secondary structure and base pairing during IVT.
  • Correlation of sequence features (e.g., unpaired bases) with dsRNA levels.
  • Development of a predictive computational model based on sequence characteristics.

Main Results:

  • Looser mRNA secondary structures and higher proportions of unpaired uracil (U) bases correlate with increased dsRNA byproduct formation.
  • A predictive model was successfully developed to forecast dsRNA levels based on mRNA sequence features.
  • Optimizing sequences based on model predictions can minimize dsRNA and enhance protein expression.

Conclusions:

  • mRNA sequence and structure significantly impact dsRNA byproduct generation during IVT.
  • A predictive model offers a novel strategy for designing high-quality mRNA therapeutics.
  • This approach facilitates the development of safer and more effective mRNA-based vaccines and therapies.